Spaces:
Sleeping
Sleeping
| # How much does it cost? | |
| AutoTrain offers an accessible approach to model training, providing deployable models | |
| with just a few clicks. Understanding the cost involved is essential to planning and | |
| executing your projects efficiently. | |
| ## Local Usage | |
| When you choose to use AutoTrain locally on your own hardware, there is no cost. | |
| This option is ideal for those who prefer to manage their own infrastructure and | |
| do not require the scalability that cloud resources offer. | |
| ## Using AutoTrain on Hugging Face Spaces | |
| **Pay-As-You-Go**: Costs for using AutoTrain in Hugging Face Spaces are based on the | |
| computing resources you consume. This flexible pricing structure ensures you only pay | |
| for what you use, making it cost-effective and scalable for projects of any size. | |
| **Ownership and Portability**: Unlike some other platforms, AutoTrain does not retain | |
| ownership of your models. Once training is complete, you are free to download and | |
| deploy your models wherever you choose, providing flexibility and control over your all your assets. | |
| ### Pricing Details | |
| **Resource-Based Billing**: Charges are accrued per minute according to the type of hardware | |
| utilized during training. This means you can scale your resource usage based on the | |
| complexity and needs of your projects. | |
| For a detailed breakdown of the costs associated with using Hugging Face Spaces, | |
| please refer to the [pricing](https://huggingface.co/pricing#spaces) section on our website. | |
| To access the paid features of AutoTrain, you must have a valid payment method on file. | |
| You can manage your payment options and view your billing information in | |
| the [billing section of your Hugging Face account settings.](https://huggingface.co/settings/billing) | |
| By offering both free and flexible paid options, AutoTrain ensures that users can choose | |
| the most suitable model training solution for their needs, whether they are experimenting | |
| on a local machine or scaling up operations on Hugging Face Spaces. | |